Hardware for artificial intelligence

Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs.^[1]

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This article needs attention from an expert in artificial intelligence. The specific problem is: Needs attention from a current expert to incorporate modern developments in this area from the last few decades, including TPUs and better coverage of GPUs, and to clean up the other material and clarify how it relates to the subject. (November 2021)

This article is missing information about its scope: What is AI hardware for the purposes of this article? Event cameras are an application of neuromorphic design, but LISP machines are not an end use application. It previously mentioned memristors, which are not specialized hardware for AI, but rather a basic electronic component, like resister, capacitor, or inductor. (November 2021)

This article needs to be updated. (November 2021)

Lisp machines

Lisp machines were developed in the late 1970s and early 1980s to make Artificial intelligence programs written in the programming language Lisp run faster.

Dataflow architecture

Dataflow architecture processors used for AI serve various purposes with varied implementations like the polymorphic dataflow^[2] Convolution Engine^[3] by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo,^[4] and dataflow scheduling by Cerebras.^[5]

Component hardware

AI accelerators

Since the 2010s, advances in computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.^[6] By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced central processing units (CPUs) as the dominant means to train large-scale commercial cloud AI.^[7] OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months.^[8]^[9]

Hardware for artificial intelligence

Lisp machines

Dataflow architecture

Component hardware

AI accelerators

Sources

Wikiwand - on